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Relevant bibliographies by topics / Diffusion MR Imaging

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Academic literature on the topic 'Diffusion MR Imaging'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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Journal articles on the topic "Diffusion MR Imaging"

1

de Figueiredo, Eduardo H. M. S. G., Arthur F. N. G. Borgonovi, and Thomas M. Doring. "Basic Concepts of MR Imaging, Diffusion MR Imaging, and Diffusion Tensor Imaging." Magnetic Resonance Imaging Clinics of North America 19, no. 1 (February 2011): 1–22. http://dx.doi.org/10.1016/j.mric.2010.10.005.

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Ramsing, B., and P. Corr. "Diffusion weighted MR imaging." South African Journal of Radiology 3, no. 3 (August 31, 1998): 4–6. http://dx.doi.org/10.4102/sajr.v3i3.1570.

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Diffusion weighted imaging (DWI) allows the measurement of molecular motion in tissue. This technique has significant clinical applications. Recent technological developments in fast MR imaging have brought diffusion imaging into clinical practice. This review will explain the physical principles, and current and future potential applications of diffusion imaging in medicine.

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Rovaris, Marco, Federica Agosta, Elisabetta Pagani, and Massimo Filippi. "Diffusion Tensor MR Imaging." Neuroimaging Clinics of North America 19, no. 1 (February 2009): 37–43. http://dx.doi.org/10.1016/j.nic.2008.08.001.

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Mutlu, H., H. O. Sildiroglu, G. Sonmez, E. Ozturk, and E. Kizilkaya. "Neuroaxonal dystrophy: MR imaging, proton MR spectroscopy, and diffusion MR imaging findings." Journal of Neuroradiology 33, no. 3 (June 2006): 207–8. http://dx.doi.org/10.1016/s0150-9861(06)77546-0.

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Kim, Hyun Jeong, Choong Gon Choi, Jeong Hyun Lee, Po Song Yang, Siwon Kang, Yeon Soo Lee, Ji Chang Kim, and Bo Seal Hwang. "Brain Diffusion Tensor MR Imaging." Journal of the Korean Radiological Society 53, no. 4 (2005): 233. http://dx.doi.org/10.3348/jkrs.2005.53.4.233.

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Le Bihan, D., R. Turner, P. Douek, and N. Patronas. "Diffusion MR imaging: clinical applications." American Journal of Roentgenology 159, no. 3 (September 1992): 591–99. http://dx.doi.org/10.2214/ajr.159.3.1503032.

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Yang, Edward, Paolo G. Nucifora, and Elias R. Melhem. "Diffusion MR Imaging: Basic Principles." Neuroimaging Clinics of North America 21, no. 1 (February 2011): 1–25. http://dx.doi.org/10.1016/j.nic.2011.02.001.

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Scarabino, T., F. Nemore, F. Esposito, F. Di Salle, S. Pollice, A. Carriero, R. Agati, and U. Salvolini. "3.0 T Diffusion MR Imaging." Rivista di Neuroradiologia 17, no. 6 (December 2004): 795–806. http://dx.doi.org/10.1177/197140090401700609.

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Corr, P., J. Keiseb, J. Moodley, C. Sanyika, M. Hoffmann, and A. Mayat. "Diffusion MR Imaging of Eclampsia." Rivista di Neuroradiologia 11, no. 2_suppl (November 1998): 167–69. http://dx.doi.org/10.1177/19714009980110s248.

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King, M. D., N. Van Bruggen, A. L. Busza, J. Houseman, S. R. Williams, and D. G. Gadian. "Perfusion and diffusion MR imaging." Magnetic Resonance in Medicine 24, no. 2 (April 1992): 288–301. http://dx.doi.org/10.1002/mrm.1910240210.

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Dissertations / Theses on the topic "Diffusion MR Imaging"

1

Skare, Stefan. "Optimisation strategies in diffusion tensor MR imaging /." Stockholm, 2002. http://diss.kib.ki.se/2002/91-7349-175-6.

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Candrák, Matúš. "Zpracování difuzně vážených obrazů pořízených MR tomografem." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220983.

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The semester thesis describes the basic principles of MRI, methods for measuring diffusion coefficients and creating DWI and DTI images. As a result a practical implementation of program was implemented in Matlab, based on theoretical knowledge of the problem.

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Bai, Y. "Correcting for motion between acquisitions in diffusion MR imaging." Thesis, University College London (University of London), 2009. http://discovery.ucl.ac.uk/18690/.

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The diffusion tensor (DT) and other diffusion models assume that each voxel corresponds to the same anatomical location in all the measurements. Movements and distortions violate this assumption and typically the images are realigned before model fitting. We propose a set of model-based methods to improve motion correction and avoid the errors that the traditional method introduces. The new methods are based on a three-step procedure to register DWI datasets, and use different reference images for DWIs with different gradient directions for registration, so the registrations take into account the contrast differences of measurements. Performance of the model-based registration techniques depends critically on outlier rejection. We develop new methods for fitting the diffusion tensor to diffusion MRI measurements in the presence of outliers by drawing on the RANSAC algorithm from computer vision. We compareone popularly used outlier rejection method RESTORE in the diffusion MRI literature with our new method. Then, we combine outlier rejection methods with model-based registration schemes, and compare the performance of motion correction with other methods. After aligning the dataset, we also update diffusion gradients for the registered datasets from both traditional and our methods, according to the transformations used in registrations. We develop and discuss a variety of registration evaluation methods using both synthetic and human-brain diffusion MRI datasets. Experiments demonstrate both quantitative and qualitative improvements using our new model-based methods.

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MacGillivray, Cathy Carleton University Dissertation Physics. "Diffusion-weighted MR imaging of moving structures using a three echo navigator imaging technique." Ottawa, 1996.

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Domenig, Claudia. "Development and evaluation of MR imaging techniques for quantitative diffusion imaging of the human pelvis." Thesis, University of Surrey, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273242.

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Kerttula, L. (Liisa). "Magnetic resonance imaging of the intervertebral disc:post-traumatic findings and the value of diffusion-weighted MR imaging." Doctoral thesis, University of Oulu, 2001. http://urn.fi/urn:isbn:9514264711.

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Abstract Magnetic resonance imaging (MRI) provides important information about structural and biochemical changes in organs. MRI is also an effective imaging method for the evaluation of spinal disorders. However, many of its potential applications - particularly diffusion imaging - have not yet been thoroughly explored. The purpose of this study was to determine the MRI-detectable changes in the intervertebral disc after trauma and to test the feasibility of diffusion-weighted MR imaging of the intervertebral discs. A minipig model was used in the experimental study to determine the MRI changes in the intervertebral disc after peripheral annular lesions in different time frames. Three of eight discs with experimental annular lesions had a normal annular appearance in MRI. Annular lesions, when detectable, were manifested as a bulging of the disc or as a high-intensity zone (HIZ) inside the annulus. Either the signal intensity or the area of bright signal intensity in the nucleus had nearly always decreased after one month, but they were still detectable even in cases where no signs of annular trauma could be seen in the MR images. The histology of HIZ is presented for the first time: clusters of nuclear cells and disorganized granulation tissue with capillaries were detected in the HIZ area. Fourteen patients 8 to 21 years of age with histories of vertebral fracture at least one year previously and 14 asymptomatic healthy control subjects 8 to 22 years of age were studied by MRI. In these young people a vertebral fracture, especially with end-plate injury, proved to be a notable risk factor for initiating disc degeneration. The apparent diffusion coefficients (ADCs) of the thoracolumbar intervertebral discs were determined in three orthogonal directions in 18 healthy young volunteers aged 8-22 years. The ADCs were also determined in 10 young patients with previous vertebral fractures, and clear decreases were found in the ADCx and ADCy directions, but in the ADCz direction values had not changed significantly as compared to the values in the controls. The most marked changes were observed in the degenerated discs, followed by those in the discs with a normal signal intensity adjacent to the primary trauma area. Diffusion-weighted MR imaging affords a useful tool for evaluating disc diseases in the early phases. Additionally, 37 adult volunteers without back symptoms were studied by MRI and by magnetic resonance angiography (MRA) and it was found that the status of the lumbar arteries significantly explained the diffusion values in the lumbar intervertebral discs. The correlation between disc degeneration and diffusion was mostly linear, but not significant.

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Gong, Nanjie, and 龔南杰. "Probing tissue microstructural changes in neurodegenerative processes using non-gaussian diffusion MR imaging." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/208583.

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Development of non-invasive imaging biomarkers sensitive to microstructural organization is crucial for deepening our understanding of mechanisms underlying neurodegenerative processes such as aging and further improving early diagnosis and monitoring of neurodegenerative disease such as Alzheimer’s disease (AD) and amnestic mild cognitive impairment (MCI). The diffusional kurtosis imaging (DKI) is an extension of conventional diffusion tensor imaging. It is hypothesized that DKI will provide complementary information to conventional diffusivity metrics in a new dimension that will more comprehensively capture microstructural changes in anisotropic white matter tracts and particularly in relatively isotropic tissues such as gray matter during neurodegenerative processing of aging, MCI and AD and probably improve the early diagnosis of the diseases. Firstly, DKI method and a white-matter model that provided metrics of explicit neurobiological interpretations were applied on healthy participants. In white matter tracts, age-related degenerations appeared to be broadly driven by axonal loss. Demyelination may also be a major driving mechanism, although confined to the anterior brain. In terms of deep gray matter, higher mean kurtosis (MK) and fractional anisotropy (FA) in the globus pallidus, substantia nigra, and red nucleus reflected higher microstructural complexity and directionality compared with the putamen, caudate nucleus, and thalamus. In particular, unique age-related positive correlations for FA, MK, and radial kurtosis (KR) in the putamen opposite to those in other regions were observed. Secondly, to verify the speculation that iron deposition could be one probable underlying mechanism driving changes in microstructure, another advance MRI technique of quantitative susceptibility mapping (QSM) was also used in healthy participants. Significant age-related increases of iron were observed in the putamen, red nucleus, substantia nigra, and caudate nucleus. Putamen exhibited the highest rate of iron accumulation with aging, which was nearly twice of the rates in substantia nigra and caudate nucleus. Significant positive correlations between susceptibility value and diffusion measurements were observed for FA and MK in the putamen as well as FA in the red nucleus. Thirdly, whether DKI metrics could serve as imaging biomarkers to indicate the severity of cognitive deficiency for AD and MCI was investigated. In AD, significantly increased diffusivity and decreased kurtosis parameters were observed in both white and gray matter of the parietal and occipital lobes as compared to MCI. Significantly decreased FA was also observed in the white matter of these lobes in AD. With the exception of FA and KR, all the other five DKI metrics exhibited significant correlations with mini-mental state examination score in both white and gray matter. Lastly, DKI metrics were compared against volumetry for diagnosis of AD and MCI. In AD vs. aMCI, although no significant difference of either FA or MD was observed in white matter tracts, it is encouraging to note that MK captured loss of microstructural complexity in the superior longitudinal fasciculus and internal capsule. MK in the putamen showed the highest power that outperformed volume of the hippocampus for discriminating AD from normal. Besides, FA in the putamen showed the second highest power for discriminating aMCI from normal.
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Diagnostic Radiology
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Doctor of Philosophy

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Coope, David John. "Use of [11C]-methionine PET and diffusion-/perfusion-weighted MR imaging in gliomas." Thesis, University of Manchester, 2010. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:207525.

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Introduction: Low-grade gliomas are a sub-group of primary brain tumours that typically affect young adults and which present specific challenges to conventional diagnostic imaging. They demonstrate a pattern of growth whereby tumour cells infiltrate healthy brain tissue without distortion of the surrounding brain or blood-brain barrier integrity. These features limit the capacity of conventional neuro-imaging strategies to effectively delineate the tumour extent or characterise the degree of 'malignancy'. One solution is to apply multiple imaging modalities to image different aspects of the tumour behaviour, analogous to histological classification based upon changes in mitotic activity, cellular atypia, microvascular proliferation and necrosis. Published information regarding how imaging techniques that address these parameters correlate within the tumour volume is limited. This reflects the technical challenges in acquiring and processing data at an adequate spatial resolution to characterise small but heterogenous tumours. In this thesis, following a series of experiments seeking to optimise the sensitivity and reproducibility of PET analysis in gliomas, a prospective multi-modal neuro-imaging study is presented addressing this need. Methods: Retrospective [11C]-methionine PET (MET PET) data made available through a collaboration with the Max-Planck Institute for Neurological Research in Cologne was carried out first to address the optimal method of analysis of PET data in gliomas. A normal methionine uptake map was created and its use in the analysis of patient scans validated against a conventional approach. Automated methods for delineating the extent of abnormal methionine uptake and identifying the region of peak uptake were developed and evaluated to optimise the reproducibility of the approach. High-resolution MET PET and a comprehensive MRI brain tumour protocol were then acquired prospectively in 20 subjects in Manchester. Detailed analysis of the peak uptake and extent of abnormal tissue defined using PET and MRI modalities including structural, diffusion- and perfusion-weighted techniques was performed. Results: Evaluation of methionine uptake with respect to population normal data, the 'RatioMap' technique, yielded peak uptake measurements that correlated closely with a conventional approach (r = 0.97) but with improved reproducibility. The constrained 3D region-growing algorithm designed to delineate the abnormal region was shown to be reproducible and to generate volumes that correlated with tumour grade. High-resolution multi-modal data in suspected low-grade gliomas demonstrated consistent correlation between peak methionine uptake ratio and peak regional cerebral blood volume (r = 0.85) but with disparity between the location of the maximal uptake regions (mean distance = 11.2mm). Significant correlation was seen between multi-modal MRI and PET ‘tumour’ volumes (r = 0.91) but with substantially larger MRI defined abnormal volumes (ratio = 2.0) including small regions identified as abnormal by multiple MRI parameters but normal on PET imaging. Conclusion: A novel method to enhance the reproducibility of analysis of MET PET images in gliomas has been presented and validated but there remains no single imaging modality capable of fully characterising glioma extent and 'malignancy' non-invasively. Considerable correlation between PET and MRI tumour biomarkers has been demonstrated but there are significant differences between the regions identified as the 'most malignant' for biopsy targeting and the extent of potentially tumour bearing tissue. Combined use of diffusion- and perfusion-weighted MRI parameters can provide results very closely correlated to the PET findings but cannot yet completely replace the use of nuclear medicine techniques. The use of multi-modal approaches to tumour characterisation as demonstrated in this study provides the most effective currently available approach to fully characterise a suspected glioma.

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Tamai, Ken. "The utility of diffusion-weighted MR imaging in the diagnosis of uterine malignancy." Kyoto University, 2008. http://hdl.handle.net/2433/135802.

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Iima, Mami. "Apparent Diffusion Coefficient as an MR Imaging Biomarker of Low-Risk Ductal Carcinoma in Situ: A Pilot Study." Kyoto University, 2014. http://hdl.handle.net/2433/188640.

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Books on the topic "Diffusion MR Imaging"

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Koh, D. M., and H. C. Thoeny, eds. Diffusion-Weighted MR Imaging. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-78576-7.

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Moritani, Toshio, Sven Ekholm, and Per-Lennart Westesson. Diffusion-Weighted MR Imaging of the Brain. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-78785-3.

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Moritani, Toshio, and Aristides A. Capizzano, eds. Diffusion-Weighted MR Imaging of the Brain, Head and Neck, and Spine. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62120-9.

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Clinical MR neuroimaging: Physiological and functional techniques. 2nd ed. New York: Cambridge University Press, 2010.

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Moritani, T., S. Ekholm, and P. L. Westesson. Diffusion-Weighted MR Imaging of the Brain. Springer, 2005.

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Moritani, Toshio, Sven Ekholm, and Per-Lennart A. Westesson. Diffusion-Weighted MR Imaging of the Brain. Springer, 2010.

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P. -L Westesson,T. Moritani,S. Ekholm. Diffusion-Weighted MR Imaging of the Brain. Springer, 2009.

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P. -L Westesson,T. Moritani,S. Ekholm. Diffusion-Weighted MR Imaging of the Brain. Springer, 2008.

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Moritani, T., S. Ekholm, and P. L. Westesson. Diffusion-Weighted MR Imaging of the Brain. Springer London, Limited, 2005.

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Diffusion-Weighted MR Imaging of the Brain. Berlin/Heidelberg: Springer-Verlag, 2005. http://dx.doi.org/10.1007/b137507.

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Book chapters on the topic "Diffusion MR Imaging"

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Tsougos, Ioannis. "Diffusion MR Imaging." In Advanced MR Neuroimaging, 1–28. Boca Raton, FL : CRC Press, Taylor & Francis Group, [2018] |: CRC Press, 2017. http://dx.doi.org/10.1201/9781351216548-1.

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Tam, Henry H., and Dow-Mu Koh. "Diffusion-Weighted MR Imaging." In Functional Imaging in Oncology, 307–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40412-2_14.

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Hajnal, J. V., and G. M. Bydder. "MR Imaging of Diffusion." In Magnetic Resonance Scanning and Epilepsy, 281–85. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2546-2_50.

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Runge, Val M., and Johannes T. Heverhagen. "Diffusion Tensor Imaging." In The Physics of Clinical MR Taught Through Images, 146–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85413-3_68.

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Runge, Val M., and Johannes T. Heverhagen. "Diffusion-Weighted Imaging." In The Physics of Clinical MR Taught Through Images, 142–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85413-3_66.

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Hall, Matt G. "The MR Physics of Advanced Diffusion Imaging." In Computational Diffusion MRI, 1–20. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54130-3_1.

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Karaosmanoğlu, Ali Devrim, Musturay Karcaaltıncaba, Mustafa N. Özmen, and Deniz Akata. "MR Imaging of Ovarian Masses." In Diffusion Weighted Imaging of the Genitourinary System, 105–23. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69575-4_5.

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Benner, Thomas, Ruopeng Wang, and J. Van Wedeen. "Diffusion Tensor Imaging of the Brain." In Parallel Imaging in Clinical MR Applications, 379–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-68879-2_34.

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Soto, Jorge A., German A. Castrillon, Stephan Anderson, and Nagaraj Holalkere. "Diffusion-Weighted MR Imaging of the Pancreas." In Diffusion MRI Outside the Brain, 99–122. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21052-5_6.

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Berman, Jeffrey I. "Advanced Diffusion MR Tractography for Surgical Planning." In Functional Brain Tumor Imaging, 183–94. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-5858-7_11.

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Conference papers on the topic "Diffusion MR Imaging"

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Zhong, Junmei, Bernard Dardzinski, Scott Holland, Janaka Wansapura, and Vincent Schmithorst. "Wavelet-based multiscale anisotropic diffusion for MR imaging." In Medical Imaging, edited by J. Michael Fitzpatrick and Joseph M. Reinhardt. SPIE, 2005. http://dx.doi.org/10.1117/12.594829.

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Li, Wu, and Jie Tian. "Automatic segmentation of brain infarction in diffusion-weighted MR images." In Medical Imaging 2003, edited by Milan Sonka and J. Michael Fitzpatrick. SPIE, 2003. http://dx.doi.org/10.1117/12.481350.

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Caruyer, Emmanuel, and Rachid Deriche. "Optimal regularization for MR diffusion signal reconstruction." In 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012). IEEE, 2012. http://dx.doi.org/10.1109/isbi.2012.6235481.

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Li, Wu, Jie Tian, and Jianping Dai. "Automatic segmentation of cerebral ischemic lesions from diffusion tensor MR images." In Medical Imaging 2004, edited by J. Michael Fitzpatrick and Milan Sonka. SPIE, 2004. http://dx.doi.org/10.1117/12.536007.

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Prima, Sylvain, and Nicolas Wiest-Daesslé. "Computation of the mid-sagittal plane in diffusion tensor MR brain images." In Medical Imaging, edited by Josien P. W. Pluim and Joseph M. Reinhardt. SPIE, 2007. http://dx.doi.org/10.1117/12.709467.

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Wu, Y., E. X. Wu, K. K. Wong, H. Tang, H. F. Tse, C. P. Lau, and E. S. Yang. "Myocardial Fiber Length Mapping with MR Diffusion Tensor Imaging." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1616118.

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Ceranka, Jakub, Mathias Polfliet, Frederic Lecouvet, Nicolas Michoux, and Jef Vandemeulebroucke. "Whole-body diffusion-weighted MR image stitching and alignment to anatomical MRI." In SPIE Medical Imaging, edited by Martin A. Styner and Elsa D. Angelini. SPIE, 2017. http://dx.doi.org/10.1117/12.2253838.

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Shi, Yundi, Gwendoline Roger, Clement Vachet, Francois Budin, Eric Maltbie, Audrey Verde, Marion Hoogstoel, Jean-Baptiste Berger, and Martin Styner. "Software-based diffusion MR human brain phantom for evaluating fiber-tracking algorithms." In SPIE Medical Imaging, edited by Sebastien Ourselin and David R. Haynor. SPIE, 2013. http://dx.doi.org/10.1117/12.2006113.

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Jha, Abhinav K., Matthew A. Kupinski, Jeffrey J. Rodríguez, Renu M. Stephen, and Alison T. Stopeck. "Evaluating segmentation algorithms for diffusion-weighted MR images: a task-based approach." In SPIE Medical Imaging, edited by David J. Manning and Craig K. Abbey. SPIE, 2010. http://dx.doi.org/10.1117/12.845515.

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Wu, Yin, and Ed X. Wu. "MR Study of Myocardial Fiber Structure Using Diffusion Tensor Imaging." In 2009 2nd International Conference on Biomedical Engineering and Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bmei.2009.5305001.

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